Novel applications of Machine Learning to Network Traffic Analysis

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Figure 3. ID-CVAE model details. ̂ For the distribution 𝒑(𝑿/𝒁, 𝑳) we use a multivariate Bernoulli distribution. The Bernoulli distribution has the interesting property of not requiring a final sampling, as the output parameter that characterizes the distribution is the mean that is the same as the probability of success. This probability can be interpreted as a [0–1] scaled value for the ground truth 𝑿, which has been already scaled to [0–1]. Then, in this case, the output of the last layer is taken ̂ as our final output 𝑿. ̂ The selection of distributions for 𝒒(𝒁/𝑿) and 𝐩(𝑿/𝒁, 𝑳) is aligned with the ones chosen in [26], they are simple and provide good results. The boxes at the lower part of Figure 3 show the specific choice of the loss function. This is a particular selection for the generic loss function presented in Figure 2. An important decision is how to incorporate the label vector in the decoder. In our case, to get the label vector inside the decoder we just concatenate it with the values of the second layer of the decoder block (Figure 3). The position for inserting the 𝑳 labels has been determined by empirical results (see Section 4.1) after considering other alternatives positions. In Figure 3, a solid arrow with a nearby X designates a fully connected layer. The numbers behind each layer designate the number of nodes of the layer. The activation function of all layers is ReLU except for the activation function of last encoder layer that is Linear and the activation function of last decoder layer which is Sigmoid. The training has been performed without dropout. 4. Results This section presents the results obtained by applying ID-CVAE and some other machine learning algorithms to the NSL-KDD dataset. A detailed evaluation of results is provided. In order to appreciate the prediction performance of the different options, and considering the highly unbalanced distribution of labels, we provide the following performance metrics: accuracy, precision, recall, F1, false positive rate (FPR) and negative predictive value (NPV). We base our definition of these performance metrics on the usually accepted ones [2]. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 135

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